Technology / Agentic Systems
In an enterprise an agent has to work every time, with observable execution, scoped permissions, benchmarked reliability and human controls.
Toolformer (Schick et al., 2023) showed language models can learn to call external tools. ReAct (Yao et al., 2023) gave a structure to reasoning-and-acting workflows. The 2024 surveys describe a field where agents specialise, collaborate and solve real tasks.
What no one has solved at scale: how do we verify what an agent can actually do, how reliably, and under what conditions? That is the work.
A useful agentic system comes out of design decisions.
It needs role design, tool boundaries, memory permissions, retrieval policies, escalation rules, evaluation frameworks, audit logs, and human-in-the-loop checkpoints. These are first-class architectural concerns. Treating them as afterthoughts is how prototypes become liabilities.
Every system we ship defines, explicitly:
Typed, scoped, with explicit task boundaries.
Per-agent access control. Not blanket capability.
Read/write rules on the Knowledge Object layer.
Which retrieval pipeline each agent uses for which task.
Orchestration, swarm, hierarchical — chosen by problem class.
Explicit conditions that route to human review.
Capability benchmarks gated behind minimum sample sizes.
Every action, tool call and reasoning step, loggable by default.
Outputs checked against schema and against KO provenance.
We make agent frameworks usable in complex enterprise contexts.
Whether you run LangGraph, CrewAI, Copilot Studio, AutoGen, Salesforce or internal tooling, we handle architecture, verification, task decomposition, evaluation, governance and integration with business knowledge: the work that turns a framework into a reliable production system.
Before an agent touches a production workflow, it needs evidence. Our verification protocol produces a measurable operating profile.
01
Capabilities, tested reliability, scoped tools and measured model independence — on one page.
02
Performance against a task battery, reported with success rates and confidence intervals.
03
Every tool the agent can reach, and the explicit boundary around each one.
04
Where and how the agent breaks — documented before it reaches production, not after.
05
The exact conditions under which control passes back to a person.
06
What gets recorded: tool calls, reasoning steps and outputs, with provenance.
07
A single, defensible go / no-go signal for deployment.
Capabilities are claims; tested reliability is evidence; tools are scoped; model independence is measured. It is the artifact a buyer inspects before integrating an agent.
agent-card.yaml
Illustrative — representative structure, not live metrics.
agent:
name: pe-intelligence-agent
version: 0.4.2
category: investment-intelligence
capabilities:
- id: deal-comparable-analysis
description: Build comparables for a target company
tested_on:
n_samples: 142
success_rate: 0.91
ci_95: [0.86, 0.95]
label: validated # not "early data"
tools:
- market-intelligence-search
- financial-data-api
- knowledge-base-read # scoped, read-only
model_independence:
tested_with: [gpt-5, claude-opus-4-7, gemini-3-pro]
swap_overhead_pct: 3.2
audit:
log: [tool_call, reasoning_step, output]
human_in_loop_on: [output_publish, escalation]Longer term, these packs feed a boutique curated catalogue of capability-verified agents — Research, Marketing and Venture verticals first. The protocol is what would make such a catalogue mean something; today it is how we make each individual agent deployable.
The verification discipline stays the same; the level of autonomy changes.
Research instrument
For R&D, science and strategy teams.
Autonomous scientific hypothesis generation. Magellan reads across silos and proposes mechanistic connections no single researcher would have found — then evaluates them on novelty, plausibility and falsifiability. Test framework execution phase, expert validation next.
Codebase modernisation
For enterprises with legacy codebases.
A multi-agent system for understanding, refactoring and modernising legacy enterprise codebases. Specialised agents handle dependency mapping, architectural analysis, refactoring planning, test generation, migration — coordinated through a shared knowledge layer.
Legacy modernisation with Catalyst →Enterprise software builder
For teams building enterprise software from spec.
Builds enterprise software from specifications. Agents for requirements, architecture, implementation, testing, deployment — with human review gates at every architectural decision point. Ready for commercial transition.
Agentic verticals
For PE, VC and corporate finance teams.
The AskMadarAI stack, decomposed into verticals: Startup Evaluator (automated diligence on early-stage companies), PE Intelligence (deal intelligence for PE workflows), Portfolio Monitor (continuous monitoring of portfolio company signals).
Agentic media intelligence
For media, comms and intelligence analysts.
Multi-agent analysis monitoring media actors, extracting claims, mapping narratives and comparing framing across outlets. Specialised agents for ingestion, claim extraction, clustering, framing analysis. Outputs that respect what the analyst is trying to do.
Architectural pattern
For compliance, research and investment review.
When one model pass is not enough: one layer retrieves and structures evidence, another classifies, another tests for contradictions, another generates output, another reviews quality. Used in investment analysis, compliance, research review, AI transformation work.
We run an internal protocol that governs the quality, readiness and security of every agent we develop. That protocol is what lets us run an enterprise-grade agent factory and automate genuinely complex processes for clients.
Every agent is exercised against task batteries before it ships. Capability is measured, not asserted.
A defined bar an agent has to clear before it touches a production workflow — sample sizes, reliability thresholds, escalation rules.
Scoped tool permissions, read/write rules on the Knowledge Object layer, full audit logs of every action and reasoning step.
Multi-step business processes that previously needed a human at every junction — decomposed, automated, with human-in-the-loop only where it earns its place.
Net asset value computation and similar operational workflows: ingestion, reconciliation, validation and reporting, with provenance on every figure.
Agents that pull from databases, documents, APIs, feeds and spreadsheets — normalising heterogeneous data into a surface the rest of the system can reason over.
Beyond extraction: contradiction detection, salience ranking and decision-ready synthesis over knowledge that is large, conflicting and ageing.
All three produce something you can inspect.
01
We review your current agent prototypes, architecture, tool permissions, data flows, logs and evaluation setup.
Output: risk map, readiness score and improvement roadmap.
02
We test one agentic workflow against a task battery — measuring reliability, cost, latency, failure modes and escalation needs.
Output: an Agent Card and a benchmark report.
03
We design and implement a production-grade agentic workflow with scoped tools, retrieval policies, audit logs, human review gates and monitoring.
Output: a monitored, inspectable production system.
If you are evaluating agent frameworks, building internal agentic workflows, or trying to take a prototype past the demo stage, start with an Agent Stack Review or a Workflow Benchmark.